2023 Seminar Series on Revenue Management and Pricing
2023年度-收益管理论坛系列报告（上）
In this seminar series, we invite a group of world-class scholars and rising stars in the area of revenue management and pricing.
Seminar format: Virtual, e.g., Tencent Meeting
Speakers: leading scholars on revenue management and pricing
Audience: Open to public
Language: Chinese (mostly) or English up to speakers
Sponsors: Southeast University School of Economics and Management
Co-Chairs: Ruxian Wang (Johns Hopkins University), Weili Xue (Southeast University)
报告人介绍
Speakers
报告一：Component Pricing with a Bundle Size Discount
报告人：Prof. Ningyuan Chen Rotman School of Management,University of Toronto
报告人简介：Dr.Ningyuan Chen is currently an assistant professor at the Department of Management at the University of Toronto, Mississauga and cross appointed at the Rotman School of Management, University of Toronto. Before joining the University of Toronto, he was an assistant professor at the Hong Kong University of Science and Technology. Prior to that, he was a postdoctoral fellow at the Yale School of Management. He received his Ph.D. from the Industrial Engineering and Operations Research (IEOR) department at Columbia University in 2015. He is interested in various approaches to making data-driven decisions in business applications such as revenue management. His studies have been published in Management Science, Operations Research, Annals of Statistics, and other journals. His research is supported by the UGC of Hong Kong and the Discovery Grants Program of Canada. He is the recipient of the Roger Martin Award for Excellence in Research and the IMI Research Award.
报告二: Recent Advancement in Healthcare Scheduling (Paradigms)
报告人：Prof. Nan Liu Boston College
报告人简介：Nan Liu is an Associate Professor of Business Analytics and holds the William S. McKiernan Family Faculty Fellowship at Carroll School of Management, Boston College. His primary research focus lies in service operations management, with a particular emphasis on healthcare, in which he studies how to match supply and demand for health services in a way that leads to timely and equitable access, cost-effectiveness, and high quality. Dr. Liu’s research has been published in leading academic journals in both fields of operations research/management and health care policy, such as Management Science, Operations Research, Manufacturing & Service Operations Management, Production and Operations Management, Health Services Research, Medical Care Research and Review, and Public Administration Review. His research and that of his students have been lauded by over a dozen awards from research and paper competitions. These include the Finalist of the 2022 Sanjay and Panna Mehrotra Research Excellence Award, First Prize of the 2021 Chinese Scholars Association for Management Science and Engineering Best Paper Competition, the 2020 Wickham-Skinner Best Paper Award, First Place of the 2019 POMS-HK International Conference Best Student Paper Competition, Winner of the 2018 POMS College of Healthcare Operations Management, Winner of the 2018 IBM Best Student Paper Award for INFORMS Service Science, and Third Place in the 2013 INFORMS Junior Faculty Interest Group Paper Competition, among others. Currently, he serves as an Associate Editor for Manufacturing & Service Operations Management, Operations Research, Health Care Management Science and Naval Research Logistics, as well as a Senior Editor for Production and Operations Management. He is a dedicated teacher and the recipient of the 2023 Coughlin Distinguished Teaching Award from Boston College Carroll School of Management.
报告三：Deep Learning Based Causal Inference for Large-Scale Combinatorial Experiments: Theory and Empirical Evidence
报告人：Prof. Renyu Zhang Chinese University of Hong Kong
报告人简介：Renyu (Philip) Zhang has been an Associate Professor (with tenure) at the Department of Decisions, Operations and Technology, The Chinese University of Hong Kong Business School since September 2022. He is also an economist and Tech Lead at Kwai, one of the world’s largest online video-sharing and live-streaming platforms.Philip’s recent research focuses on developing datascience methodologies (e.g., data-driven optimization, causal inference, and machine learning) to evaluate and optimize the operations strategies in the contexts of online platforms and marketplaces, sharing economy, and social networks, especially their recommendation, advertising, pricing, and matching policies. His research works have appeared in top journals such as Management Science, Operations Research, and Manufacturing & Service Operations Management, and have been recognized by various research awards of the INFORMS, POMS, and CSAMSE communities. His research projects have been funded by various funding agencies including HK RGC, NSFC, SMEC, STCSM, and Tencent.Philip serves as a Senior Editor for Production and Operations Management, and an Associate Editor for Naval Research Logistics. He has also developed data science and economics frameworks to evaluate and optimize the user growth strategy and the platform ecosystem of Kwai. Prior to joining CUHK, Philip was an Assistant Professor of Operations Management at New York University Shanghai between 2016 and 2022. Please visit Philip’s personal website for more about him: https://rphilipzhang.github.io/rphilipzhang/
报告四：Online Learning for Pricing in On-Demand Vehicle Sharing Networks
报告人：Prof. Huanan Zhang University of Colorado Boulder
报告人简介： Huanan Zhang is an Assistant Professor in the Leeds School of Business at the University of Colorado Boulder. Before joining Leeds, he was an Assistant Professor in the Department of Industrial and Manufacturing Engineering at Penn State University. He received Ph.D. in Industrial and Operation Engineering at the University of Michigan in 2017. Huanan’s research interests include the design of data-driven algorithms, approximation algorithms, and their applications in inventory and supply chain management, revenue management, and service operations. His research has been published in leading Operations Management journals like Management Science, Operations Research, Manufacturing & Service Operations Management, and Production and Operations Management.
报告时间安排
Agenda
主持人：王汝现教授（约翰霍普金斯大学）、薛巍立教授（东南大学）
时间：2022/08/15（Tue.）9:00-11:00(上午AM)
报告人：Prof. Ningyuan Chen
Rotman School of Management,University of Toronto
主题：Component Pricing with a Bundle Size Discount
腾讯会议：100-293-012
摘要：Firms selling multiple products usually adopt bundle pricing in their marketing strategy for the purpose of extracting large consumer surplus. In this paper, we propose and analyze a bundling mechanism, referred to as component pricing with a bundle size discount (CPBSD), which sells bundles for the summed prices of the included products (component pricing) minus a discount based on the number of products purchased (bundle size discount).CPBSD} is conceptually simple and has been widely used in real-world business settings. Theoretically, we show that CPBSD subsumes several most well-studied bundling mechanisms including component pricing, pure bundling, and bundle size pricing as special cases. We further prove that, under a general condition, CPBSD attains the optimal profit asymptotically for a large number of products among all bundle pricing mechanisms. From a practical perspective, we formulate a mixed-integer linear program for the optimal pricing scheme of CPBSD, and we also develop an approximation algorithm for efficiently solving CPBSD in large-scale problems. Through comprehensive numerical experiments, we show that CPBSD demonstrates superior performance in contrast to other bundling mechanisms. In particular, compared to bundle size pricing whose outstanding empirical performance has been extensively tested in the literature, CPBSD performs especially well when products are heterogeneous and the production costs are high. Furthermore, the performance of CPBSD is enhenced significantly when the potential surplus provided by products are negatively correlated with the product valuations. We also show that our approximation algorithm may achieve a performance that is very close to the optimal CPBSD. Given the theoretical guarantee and the computational efficiency, CPBSD presents an appealing selling mechanism for retailers to improve their profitability.
时间：2023/08/23（Wes.）9:00-11:00(上午AM)
报告人：Prof. Nan Liu
Boston College
主题：Recent Advancement in Healthcare Scheduling (Paradigms)
腾讯会议：341-921-112
摘要: Scheduling is the core function of healthcare operations to match provider service capacity and patient demand in a safe, efficient, and equitable way. How to schedule patients is a classic operations problem that has been studied for decades. In recent years, the topic has gained renewed interest due to advancement in clinical practice, data availability, and analytics methodologies. In this seminar I will talk about two recent projects that are motivated by operational challenges in health care delivery: one for designing patient visit itineraries in complex outpatient care settings and the other for managing hospital diagnostic services in inpatient care. Both projects lead to the development of new scheduling paradigms not yet studied before and therefore require state-of-the-art theoretical treatment. I will discuss the models, structural properties, solution approaches and case studies based on data from our collaborating organizations.
时间：2023/08/28（Mon.）9:00-11:00(上午AM)
报告人：Prof. Renyu Zhang
Chinese University of Hong Kong
主题：Deep Learning Based Causal Inference for Large-Scale Combinatorial Experiments: Theory and Empirical Evidence
腾讯会议：609-523-094
摘要：Large-scale online platforms launch hundreds of randomized experiments (a.k.a. A/B tests) every day to iterate their operations and marketing strategies, while the combinations of these treatments are typically not exhaustively tested. It triggers an important question of both academic and practical interests: Without observing the outcomes of all treatment combinations, how to estimate the causal effect of any treatment combination and identify the optimal treatment combination? We develop a novel framework combining deep learning and doubly robust estimation to estimate the causal effect of any treatment combination for each user on the platform when observing only a small subset of treatment combinations. Our proposed framework (called debiased deep learning, DeDL) exploits Neyman orthogonality and combines interpretable and flexible structural layers in deep learning. We prove theoretically that this framework yields efficient, consistent, and asymptotically normal estimators under mild assumptions, thus allowing for identifying the best treatment combination when only observing a few combinations. To empirically validate our method, we then collaborate with a large-scale video-sharing platform and implement our framework for three experiments involving three treatments where each combination of treatments is tested. When only observing a subset of treatment combinations, our DeDL approach significantly outperforms other benchmarks to accurately estimate and infer the average treatment effect (ATE) of any treatment combination, and to identify the optimal treatment combination.
时间：2023/09/29（Fri.）9:00-11:00(上午AM)
报告人：Prof. Huanan Zhang
Leeds School of Business,
University of Colorado Boulder
主题：Online Learning for Pricing in On-Demand Vehicle Sharing Networks
腾讯会议：695-909-596
摘要：We consider the pricing problem in on-demand vehicle sharing networks with online demand learning. When there is no prior information available on the demand functions, the main challenge in designing an online learning algorithm is how to explore the demand functions while maintaining a balanced network. We address this challenge with an online learning algorithm adapted from the ellipsoid method. In our algorithm, the search subroutine is based on the idea of bisection and the Upper Confidence Bound, which can locate the price associated with a desired demand level for each type of trip, as characterized by the trip origin and destination, and estimate the gradient information at the price point. By carefully selecting the center of the ellipsoid for each iteration, we can ensure that the expected revenue improves and maintain a balanced network in each iteration. We prove that the regret of our learning algorithm is bounded by given a fixed workload parameter . The numerical performance of the algorithm is illustrated using synthetic data. We also discuss extensions to the online learning algorithm in which the workload parameter is unknown.